This hands-on course introduces the participants to RNA-seq data analysis methods, tools and file formats. It covers the whole workflow from quality control and alignment to quantification and differential expression analysis. Both whole transcript and QuantSeq 3' UMI data are covered. QuantSeq data analysis involves different preprocessing, so the full session on 8.4.2022 is dedicated to analyzing QuantSeq data and you have the option to register for that day only if you are already familiar with the othe topics (please see the details below).The course consists of lectures and exercises. The lectures will be pre-recorded, and participants are requested to view the videos prior to the course and test their knowledge with a set of questions. This gives you more time to reflect on the concepts so that you can use the course time more efficiently for discussions and exercises. Note that the lectures specific to QuantSeq data are given during the course on 8.4.2022.The course takes place at 9-12 Helsinki time (8-11 CET) each day in Zoom.PrerequisitiesIn the exercises we use analysis tools embedded in the free and user-friendly Chipster software, so no previous knowledge of Unix or R is required, and the course is thus suitable for everybody who is planning to use RNA-seq.Content6.4.2022 at 9-12: Quality control, trimming and alignmentcheck the quality of reads with MultiQCremove bad quality data with Trimmomaticinfer strandedness with RseQCalign RNA-seq reads to the reference genome with HISAT2 and STARefficient analysis: how to assign paired FASTQ files to samples and align all the samples with one clickperform alignment level quality control using RseQC7.4.2022 at 9-12: Quantifying expression, experiment level QC, differential expression analysisquantify expression by counting reads per genes using HTSeqcheck the experiment level quality with PCA plots and heatmapsanalyze differential expression with DESeq2 and edgeRtake multiple factors (including batch effects) into account in differential expression analysisproduce heatmaps of differentially expressed geneshow to share analysis with a colleague8.4.2022 at 9-12: QuantSeq data analysisMultiQC: how to detect UMI, TATA and polyA readthrough and adaptersextract UMI with UMI-toolsremove polyA readthrough, adapters and bad quality ends with BBDukalign RNA-seq reads to the reference genome with STARdeduplicate alignments using UMI-toolsnote that in the exercises we practise the full workflow, which includes also strandedness inference, quantitation, experiment level QC and differential expression analysis. TrainersEija Korpelainen (CSC), Maria Lehtivaara (CSC)Course materialsBefore the course you will get access to the course videos available in Chipster Youtube channel. Slides and exercises will be shared on during the course.Price 60eur